Poster No:
2089
Submission Type:
Abstract Submission
Authors:
Yaser Sánchez Gama1,2, Mengyuan Ding1,2, Michael Ferguson1,2
Institutions:
1Neurospirituality Lab, Center for Brain Circuit Therapeutics, Boston, MA, 2Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA, Boston, MA
First Author:
Yaser Sánchez Gama
Neurospirituality Lab, Center for Brain Circuit Therapeutics|Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Boston, MA|Boston, MA
Co-Author(s):
Mengyuan Ding, MD, MMSCI
Neurospirituality Lab, Center for Brain Circuit Therapeutics|Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Boston, MA|Boston, MA
Michael Ferguson, Ph.D
Neurospirituality Lab, Center for Brain Circuit Therapeutics|Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Boston, MA|Boston, MA
Introduction:
Introduction: For centuries, brain lesion studies have offered causal insights into neurological and psychiatric phenomena (Fox, 2018; Joutsa et al., 2022; Boes et al., 2015; Siddiqi et al., 2022). Although functional neuroimaging has enhanced our understanding of brain function, its correlational nature limits therapeutic translation. Lesion Network Mapping (LNM)-which integrates lesion data with normative connectivity -has identified common networks for disparate lesion locations causing symptoms like visual hallucinations, amnesia, and hemichorea (Joutsa et al., 2022; Boes et al., 2015; Siddiqi et al., 2022). Here, we apply LNM to hypersomnia, a group of disorders characterized by excessive daytime sleepiness and reduced quality of life (Dauvilliers et al., 2018)
Methods:
Methods: A systematic review conducted from June to October 2024 identified stroke-related cases reporting hypersomnia. After applying rigorous inclusion criteria, 63 cases were selected, and their lesions were mapped onto an MNI atlas. Lesion locations were then correlated with a normative functional connectome (n=1,000)(Holmes et al., 2015). We identified regions consistently connected in more than 95% of the cases and confirmed the robustness of these connections using two statistical thresholds (t > 4.7 and t > 7; FWE p < 0.05), as well as one-sample Welch's t-tests corrected for multiple comparisons. In addition, a split-half replication (n1 = 31, n2 = 32) was conducted to validate our findings. To assess specificity, the identified network was tested against two independent datasets (n=135 and n=996) using a two-sample t-test (FWE p < 0.05), ensuring specificity to lesions associated with hypersomnia. A conjunction map was then computed, combining results from sensitivity, specificity, and consistency testing. Additional validation involved examining the whole-brain connectivity of the conjunction map to confirm overlap with individual lesions and previously known wakefulness-related nodes. Finally, the hypothesis of preferential functional connectivity of the posterior hypothalamus compared to the anterior hypothalamus across all cases was assessed.
Results:
Results: Despite heterogeneous lesion locations, hypersomnia-causing lesions converged on a common brain circuit (> 95% of cases) encompassing regions of the medulla, cerebellum, pons, midbrain, subthalamic nucleus, hypothalamus, thalamus, globus pallidus, putamen, caudate nucleus, bilateral anterior insula, anterior cingulate cortex, and right frontal cortex. These connections persisted across varying thresholds and randomized subsamples. Specificity testing demonstrated that this pattern was unique to hypersomnia. A final conjunction map highlighted well-established arousal centers, including the posterior and lateral hypothalamus and the tuberomammillary nucleus, aligning with known wakefulness pathways. All lesions (n = 63, 100%) converged on this conjunction map. All nodes from the Harvard Ascending Arousal Atlas (Edlow et al., 2024) were encompassed by the functional connectivity of the conjunction map. Moreover, connectivity to the posterior hypothalamus (μ = 22.73) was significantly stronger than to anterior hypothalamic regions (μ = 1.38), t(63) = 23.09, p = 3.745 × 10⁻³², consistent with classical sleep-wake regulation models (Saper et al., 2010).
Conclusions:
Conclusions: These findings reveal that hypersomnia results from the disruption of a well-defined, functionally connected brain network rather than damage to a single, localized area. By pinpointing a common circuit involved in hypersomnia, our study underscores the utility of LNM in understanding complex neuropsychiatric symptoms. This network-level framework may guide future therapeutic interventions, such as identifying new stimulation targets for Deep Brain Stimulation (DBS) or Transcranial Magnetic Stimulation (TMS), aimed at restoring normal wakefulness and improving quality of life for patients with hypersomnia-related disorders.
Brain Stimulation:
Non-Invasive Stimulation Methods Other
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
FUNCTIONAL MRI
Sleep
Transcranial Magnetic Stimulation (TMS)
Other - Wakefulness
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Please indicate which methods were used in your research:
Functional MRI
Neurophysiology
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References:
Dauvilliers, Y., Bogan, R. K., Arnulf, I., Scammell, T. E., St Louis, E. K., & Thorpy, M. J. (2022). Clinical considerations for the diagnosis of idiopathic hypersomnia. Sleep medicine reviews, 66, 101709. https://doi.org/10.1016/j.smrv.2022.101709
Edlow, B. L., Olchanyi, M., Freeman, H. J., Li, J., Maffei, C., Snider, S. B., Zöllei, L., Iglesias, J. E., Augustinack, J., Bodien, Y. G., Haynes, R. L., Greve, D. N., Diamond, B. R., Stevens, A., Giacino, J. T., Destrieux, C., van der Kouwe, A., Brown, E. N., Folkerth, R. D., Fischl, B., … Kinney, H. C. (2024). Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness. Science translational medicine, 16(745), eadj4303. https://doi.org/10.1126/scitranslmed.adj4303
Fox M. D. (2018). Mapping Symptoms to Brain Networks with the Human Connectome. The New England journal of medicine, 379(23), 2237–2245. https://doi.org/10.1056/NEJMra1706158
Holmes, A. J., Hollinshead, M. O., O'Keefe, T. M., Petrov, V. I., Fariello, G. R., Wald, L. L., Fischl, B., Rosen, B. R., Mair, R. W., Roffman, J. L., Smoller, J. W., & Buckner, R. L. (2015). Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Scientific data, 2, 150031. https://doi.org/10.1038/sdata.2015.31
Joutsa, J., Corp, D. T., & Fox, M. D. (2022). Lesion network mapping for symptom localization: recent developments and future directions. Current opinion in neurology, 35(4), 453–459. https://doi.org/10.1097/WCO.0000000000001085
Saper, C. B., Fuller, P. M., Pedersen, N. P., Lu, J., & Scammell, T. E. (2010). Sleep state switching. Neuron, 68(6), 1023–1042. https://doi.org/10.1016/j.neuron.2010.11.032
Scammell, T. E., Arrigoni, E., & Lipton, J. O. (2017). Neural Circuitry of Wakefulness and Sleep. Neuron, 93(4), 747–765. https://doi.org/10.1016/j.neuron.2017.01.014
Siddiqi, S. H., Kording, K. P., Parvizi, J., & Fox, M. D. (2022). Causal mapping of human brain function. Nature reviews. Neuroscience, 23(6), 361–375. https://doi.org/10.1038/s41583-022-00583-8
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